Prediction-based system and method for trajectory planning of autonomous vehicles

ABSTRACT

A prediction-based system and method for trajectory planning of autonomous vehicles are disclosed. A particular embodiment is configured to: receive training data and ground truth data from a training data collection system, the training data including perception data and context data corresponding to human driving behaviors; perform a training phase for training a trajectory prediction module using the training data; receive perception data associated with a host vehicle; and perform an operational phase for extracting host vehicle feature data and proximate vehicle context data from the perception data, generating a proposed trajectory for the host vehicle, using the trained trajectory prediction module to generate predicted trajectories for each of one or more proximate vehicles near the host vehicle based on the proposed host vehicle trajectory, determining if the proposed trajectory for the host vehicle will conflict with any of the predicted trajectories of the proximate vehicles, and modifying the proposed trajectory for the host vehicle until conflicts are eliminated.

PRIORITY PATENT APPLICATIONS

This is a continuation patent application drawing priority from U.S.non-provisional patent application Ser. No. 15/806,013; filed Nov. 7,2017; which is a continuation-in-part (CIP) patent application drawingpriority from U.S. non-provisional patent application Ser. No.15/698,607; filed Sep. 7, 2017. This present non-provisional CIP patentapplication draws priority from the referenced patent applications. Theentire disclosure of the referenced patent applications is consideredpart of the disclosure of the present application and is herebyincorporated by reference herein in its entirety.

COPYRIGHT NOTICE

A portion of the disclosure of this patent document contains materialthat is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure, as it appears in the U.S. Patent and TrademarkOffice patent files or records, but otherwise reserves all copyrightrights whatsoever. The following notice applies to the disclosure hereinand to the drawings that form a part of this document: Copyright2016-2020, TuSimple, Inc., All Rights Reserved.

TECHNICAL FIELD

This patent document pertains generally to tools (systems, apparatuses,methodologies, computer program products, etc.) for trajectory planning,vehicle control systems, and autonomous driving systems, and moreparticularly, but not by way of limitation, to a prediction-based systemand method for trajectory planning of autonomous vehicles.

BACKGROUND

An autonomous vehicle is often configured to follow a trajectory basedon a computed driving path. However, when variables such as obstaclesare present on the driving path, the autonomous vehicle must performcontrol operations so that the vehicle may be safely driven by changingthe driving path to avoid the obstacles.

In the related art, autonomous vehicle control operations have beendetermined by representing spatial information (e.g., a coordinate, aheading angle, a curvature, etc.) of the driving path as a polynomialexpression or mathematical function for a movement distance in order toavoid a stationary obstacle. However, when dynamic obstacles are presenton the driving path, the autonomous vehicle according to the related artmay not accurately predict whether or not the vehicle will collide withthe dynamic obstacles. In particular, the related art does not considerthe interaction between the autonomous vehicle and other dynamicvehicles. Therefore, conventional autonomous vehicle control systemscannot accurately predict the future positions of other proximatedynamic vehicles. As a result, the optimal behavior of the conventionalautonomous vehicle cannot be achieved. For example, the unexpectedbehavior of a proximate dynamic obstacle may result in a collision withthe conventional autonomous vehicle.

SUMMARY

A prediction-based system and method for trajectory planning ofautonomous vehicles is disclosed herein. Specifically, the presentdisclosure relates to trajectory planning for autonomous vehicles usinga prediction-based method. In one aspect, the system herein may includevarious sensors configured to collect perception data, a computingdevice, and a trajectory prediction module for predicting a trajectoryof other vehicles and/or dynamic objects in the vicinity of (proximateto) a host autonomous vehicle. Initially, the computing device generatesa trajectory option, while the trajectory prediction module predictsreactions of vehicles and/or dynamic objects examined using dataconcerning likely trajectories of each vehicle and/or dynamic objectsrespectively. Data corresponding to predicted reactions can be sent tothe computing device to perfect the trajectory option suggestedinitially. The computing device may subsequently instruct the trajectoryprediction module to further collect data and conduct predictions tocomplete the trajectory planning process.

BRIEF DESCRIPTION OF THE DRAWINGS

The various embodiments are illustrated by way of example, and not byway of limitation, in the figures of the accompanying drawings in which:

FIG. 1 illustrates a block diagram of an example ecosystem in which aprediction-based trajectory planning module of an example embodiment canbe implemented;

FIG. 2 illustrates an offline training phase for training and building aprediction-based trajectory planning system in an example embodiment;

FIGS. 3 and 4 illustrate examples of the context data used to train thetrajectory prediction module in the offline training phase;

FIG. 5 illustrates an operational or processing workflow for the offlinetraining of the trajectory prediction module in an example embodiment;

FIG. 6 illustrates an example embodiment of the components of theprediction-based trajectory planning system and the prediction-basedtrajectory planning module therein;

FIGS. 7 and 8 illustrate an operational or processing workflow for theoperational phase use of the prediction-based trajectory planning systemin an example embodiment;

FIG. 9 is a process flow diagram illustrating an example embodiment of aprediction-based system and method for trajectory planning of autonomousvehicles;

FIG. 10 is a process flow diagram illustrating an alternative exampleembodiment of a prediction-based system and method for trajectoryplanning of autonomous vehicles; and

FIG. 11 shows a diagrammatic representation of machine in the exampleform of a computer system within which a set of instructions whenexecuted may cause the machine to perform any one or more of themethodologies discussed herein.

DETAILED DESCRIPTION

In the following description, for purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the various embodiments. It will be evident, however,to one of ordinary skill in the art that the various embodiments may bepracticed without these specific details.

As described in various example embodiments, a prediction-based systemand method for trajectory planning of autonomous vehicles are describedherein. An example embodiment disclosed herein can be used in thecontext of an in-vehicle control system 150 in a vehicle ecosystem 101shown in FIG. 1. In one example embodiment, an in-vehicle control system150 with a prediction-based trajectory planning module 200 resident in avehicle 105 can be configured like the architecture and ecosystem 101illustrated in FIG. 1. However, it will be apparent to those of ordinaryskill in the art that the prediction-based trajectory planning module200 described and claimed herein can be implemented, configured, andused in a variety of other applications and systems as well.

Referring now to FIG. 1, a block diagram illustrates an exampleecosystem 101 in which an in-vehicle control system 150 and aprediction-based trajectory planning module 200 of an example embodimentcan be implemented. These components are described in more detail below.Ecosystem 101 includes a variety of systems and components that cangenerate and/or deliver one or more sources of information/data andrelated services to the in-vehicle control system 150 and theprediction-based trajectory planning module 200, which can be installedin the vehicle 105. For example, a camera installed in the vehicle 105,as one of the devices of vehicle subsystems 140, can generate image andtiming data that can be received by the in-vehicle control system 150.The in-vehicle control system 150 and an image processing moduleexecuting therein can receive this image and timing data input. Theimage processing module can extract object data from the image andtiming data to identify objects in the proximity of the vehicle. Asdescribed in more detail below, the prediction-based trajectory planningmodule 200 can process the object data and generate a trajectory for thevehicle based on the detected objects. The trajectory can be used by anautonomous vehicle control subsystem, as another one of the subsystemsof vehicle subsystems 140. The autonomous vehicle control subsystem, forexample, can use the real-time generated trajectory to safely andefficiently navigate the vehicle 105 through a real world drivingenvironment while avoiding obstacles and safely controlling the vehicle.

In an example embodiment as described herein, the in-vehicle controlsystem 150 can be in data communication with a plurality of vehiclesubsystems 140, all of which can be resident in a user's vehicle 105. Avehicle subsystem interface 141 is provided to facilitate datacommunication between the in-vehicle control system 150 and theplurality of vehicle subsystems 140. The in-vehicle control system 150can be configured to include a data processor 171 to execute theprediction-based trajectory planning module 200 for processing objectdata received from one or more of the vehicle subsystems 140. The dataprocessor 171 can be combined with a data storage device 172 as part ofa computing system 170 in the in-vehicle control system 150. The datastorage device 172 can be used to store data, processing parameters, anddata processing instructions. A processing module interface 165 can beprovided to facilitate data communications between the data processor171 and the prediction-based trajectory planning module 200. In variousexample embodiments, a plurality of processing modules, configuredsimilarly to prediction-based trajectory planning module 200, can beprovided for execution by data processor 171. As shown by the dashedlines in FIG. 1, the prediction-based trajectory planning module 200 canbe integrated into the in-vehicle control system 150, optionallydownloaded to the in-vehicle control system 150, or deployed separatelyfrom the in-vehicle control system 150.

The in-vehicle control system 150 can be configured to receive ortransmit data from/to a wide-area network 120 and network resources 122connected thereto. An in-vehicle web-enabled device 130 and/or a usermobile device 132 can be used to communicate via network 120. Aweb-enabled device interface 131 can be used by the in-vehicle controlsystem 150 to facilitate data communication between the in-vehiclecontrol system 150 and the network 120 via the in-vehicle web-enableddevice 130. Similarly, a user mobile device interface 133 can be used bythe in-vehicle control system 150 to facilitate data communicationbetween the in-vehicle control system 150 and the network 120 via theuser mobile device 132. In this manner, the in-vehicle control system150 can obtain real-time access to network resources 122 via network120. The network resources 122 can be used to obtain processing modulesfor execution by data processor 171, data content to train internalneural networks, system parameters, or other data.

The ecosystem 101 can include a wide area data network 120. The network120 represents one or more conventional wide area data networks, such asthe Internet, a cellular telephone network, satellite network, pagernetwork, a wireless broadcast network, gaming network, WiFi network,peer-to-peer network, Voice over IP (VoIP) network, etc. One or more ofthese networks 120 can be used to connect a user or client system withnetwork resources 122, such as websites, servers, central control sites,or the like. The network resources 122 can generate and/or distributedata, which can be received in vehicle 105 via in-vehicle web-enableddevices 130 or user mobile devices 132. The network resources 122 canalso host network cloud services, which can support the functionalityused to compute or assist in processing object input or object inputanalysis. Antennas can serve to connect the in-vehicle control system150 and the prediction-based trajectory planning module 200 with thedata network 120 via cellular, satellite, radio, or other conventionalsignal reception mechanisms. Such cellular data networks are currentlyavailable (e.g., Verizon™, AT&T™, T-Mobile™, etc.). Such satellite-baseddata or content networks are also currently available (e.g., SiriusXM™,HughesNet™, etc.). The conventional broadcast networks, such as AM/FMradio networks, pager networks, UHF networks, gaming networks, WiFinetworks, peer-to-peer networks, Voice over IP (VoIP) networks, and thelike are also well-known. Thus, as described in more detail below, thein-vehicle control system 150 and the prediction-based trajectoryplanning module 200 can receive web-based data or content via anin-vehicle web-enabled device interface 131, which can be used toconnect with the in-vehicle web-enabled device receiver 130 and network120. In this manner, the in-vehicle control system 150 and theprediction-based trajectory planning module 200 can support a variety ofnetwork-connectable in-vehicle devices and systems from within a vehicle105.

As shown in FIG. 1, the in-vehicle control system 150 and theprediction-based trajectory planning module 200 can also receive data,object processing control parameters, and training content from usermobile devices 132, which can be located inside or proximately to thevehicle 105. The user mobile devices 132 can represent standard mobiledevices, such as cellular phones, smartphones, personal digitalassistants (PDA's), MP3 players, tablet computing devices (e.g., iPad™),laptop computers, CD players, and other mobile devices, which canproduce, receive, and/or deliver data, object processing controlparameters, and content for the in-vehicle control system 150 and theprediction-based trajectory planning module 200. As shown in FIG. 1, themobile devices 132 can also be in data communication with the networkcloud 120. The mobile devices 132 can source data and content frominternal memory components of the mobile devices 132 themselves or fromnetwork resources 122 via network 120. Additionally, mobile devices 132can themselves include a GPS data receiver, accelerometers, WiFitriangulation, or other geo-location sensors or components in the mobiledevice, which can be used to determine the real-time geo-location of theuser (via the mobile device) at any moment in time. In any case, thein-vehicle control system 150 and the prediction-based trajectoryplanning module 200 can receive data from the mobile devices 132 asshown in FIG. 1.

Referring still to FIG. 1, the example embodiment of ecosystem 101 caninclude vehicle operational subsystems 140. For embodiments that areimplemented in a vehicle 105, many standard vehicles include operationalsubsystems, such as electronic control units (ECUs), supportingmonitoring/control subsystems for the engine, brakes, transmission,electrical system, emissions system, interior environment, and the like.For example, data signals communicated from the vehicle operationalsubsystems 140 (e.g., ECUs of the vehicle 105) to the in-vehicle controlsystem 150 via vehicle subsystem interface 141 may include informationabout the state of one or more of the components or subsystems of thevehicle 105. In particular, the data signals, which can be communicatedfrom the vehicle operational subsystems 140 to a Controller Area Network(CAN) bus of the vehicle 105, can be received and processed by thein-vehicle control system 150 via vehicle subsystem interface 141.Embodiments of the systems and methods described herein can be used withsubstantially any mechanized system that uses a CAN bus or similar datacommunications bus as defined herein, including, but not limited to,industrial equipment, boats, trucks, machinery, or automobiles; thus,the term “vehicle” as used herein can include any such mechanizedsystems. Embodiments of the systems and methods described herein canalso be used with any systems employing some form of network datacommunications; however, such network communications are not required.

Referring still to FIG. 1, the example embodiment of ecosystem 101, andthe vehicle operational subsystems 140 therein, can include a variety ofvehicle subsystems in support of the operation of vehicle 105. Ingeneral, the vehicle 105 may take the form of a car, truck, motorcycle,bus, boat, airplane, helicopter, lawn mower, earth mover, snowmobile,aircraft, recreational vehicle, amusement park vehicle, farm equipment,construction equipment, tram, golf cart, train, and trolley, forexample. Other vehicles are possible as well. The vehicle 105 may beconfigured to operate fully or partially in an autonomous mode. Forexample, the vehicle 105 may control itself while in the autonomousmode, and may be operable to determine a current state of the vehicleand its context in its environment, determine a predicted behavior of atleast one other vehicle in the context of the environment, determine aconfidence level that may correspond to a likelihood of the at least oneother vehicle to perform the predicted behavior, and control the vehicle105 based on the determined information. While in autonomous mode, thevehicle 105 may be configured to operate without human interaction.

The vehicle 105 may include various vehicle subsystems such as a vehicledrive subsystem 142, vehicle sensor subsystem 144, vehicle controlsubsystem 146, and occupant interface subsystem 148. As described above,the vehicle 105 may also include the in-vehicle control system 150, thecomputing system 170, and the prediction-based trajectory planningmodule 200. The vehicle 105 may include more or fewer subsystems andeach subsystem could include multiple elements. Further, each of thesubsystems and elements of vehicle 105 could be interconnected. Thus,one or more of the described functions of the vehicle 105 may be dividedup into additional functional or physical components or combined intofewer functional or physical components. In some further examples,additional functional and physical components may be added to theexamples illustrated by FIG. 1.

The vehicle drive subsystem 142 may include components operable toprovide powered motion for the vehicle 105. In an example embodiment,the vehicle drive subsystem 142 may include an engine or motor,wheels/tires, a transmission, an electrical subsystem, and a powersource. The engine or motor may be any combination of an internalcombustion engine, an electric motor, steam engine, fuel cell engine,propane engine, or other types of engines or motors. In some exampleembodiments, the engine may be configured to convert a power source intomechanical energy. In some example embodiments, the vehicle drivesubsystem 142 may include multiple types of engines or motors. Forinstance, a gas-electric hybrid car could include a gasoline engine andan electric motor. Other examples are possible.

The wheels of the vehicle 105 may be standard tires. The wheels of thevehicle 105 may be configured in various formats, including a unicycle,bicycle, tricycle, or a four-wheel format, such as on a car or a truck,for example. Other wheel geometries are possible, such as thoseincluding six or more wheels. Any combination of the wheels of vehicle105 may be operable to rotate differentially with respect to otherwheels. The wheels may represent at least one wheel that is fixedlyattached to the transmission and at least one tire coupled to a rim ofthe wheel that could make contact with the driving surface. The wheelsmay include a combination of metal and rubber, or another combination ofmaterials. The transmission may include elements that are operable totransmit mechanical power from the engine to the wheels. For thispurpose, the transmission could include a gearbox, a clutch, adifferential, and drive shafts. The transmission may include otherelements as well. The drive shafts may include one or more axles thatcould be coupled to one or more wheels. The electrical system mayinclude elements that are operable to transfer and control electricalsignals in the vehicle 105. These electrical signals can be used toactivate lights, servos, electrical motors, and other electricallydriven or controlled devices of the vehicle 105. The power source mayrepresent a source of energy that may, in full or in part, power theengine or motor. That is, the engine or motor could be configured toconvert the power source into mechanical energy. Examples of powersources include gasoline, diesel, other petroleum-based fuels, propane,other compressed gas-based fuels, ethanol, fuel cell, solar panels,batteries, and other sources of electrical power. The power source couldadditionally or alternatively include any combination of fuel tanks,batteries, capacitors, or flywheels. The power source may also provideenergy for other subsystems of the vehicle 105.

The vehicle sensor subsystem 144 may include a number of sensorsconfigured to sense information about an environment or condition of thevehicle 105. For example, the vehicle sensor subsystem 144 may includean inertial measurement unit (IMU), a Global Positioning System (GPS)transceiver, a RADAR unit, a laser range finder/LIDAR unit, and one ormore cameras or image capture devices. The vehicle sensor subsystem 144may also include sensors configured to monitor internal systems of thevehicle 105 (e.g., an 02 monitor, a fuel gauge, an engine oiltemperature). Other sensors are possible as well. One or more of thesensors included in the vehicle sensor subsystem 144 may be configuredto be actuated separately or collectively in order to modify a position,an orientation, or both, of the one or more sensors.

The IMU may include any combination of sensors (e.g., accelerometers andgyroscopes) configured to sense position and orientation changes of thevehicle 105 based on inertial acceleration. The GPS transceiver may beany sensor configured to estimate a geographic location of the vehicle105. For this purpose, the GPS transceiver may include areceiver/transmitter operable to provide information regarding theposition of the vehicle 105 with respect to the Earth. The RADAR unitmay represent a system that utilizes radio signals to sense objectswithin the local environment of the vehicle 105. In some embodiments, inaddition to sensing the objects, the RADAR unit may additionally beconfigured to sense the speed and the heading of the objects proximateto the vehicle 105. The laser range finder or LIDAR unit may be anysensor configured to sense objects in the environment in which thevehicle 105 is located using lasers. In an example embodiment, the laserrange finder/LIDAR unit may include one or more laser sources, a laserscanner, and one or more detectors, among other system components. Thelaser range finder/LIDAR unit could be configured to operate in acoherent (e.g., using heterodyne detection) or an incoherent detectionmode. The cameras may include one or more devices configured to capturea plurality of images of the environment of the vehicle 105. The camerasmay be still image cameras or motion video cameras.

The vehicle control system 146 may be configured to control operation ofthe vehicle 105 and its components. Accordingly, the vehicle controlsystem 146 may include various elements such as a steering unit, athrottle, a brake unit, a navigation unit, and an autonomous controlunit.

The steering unit may represent any combination of mechanisms that maybe operable to adjust the heading of vehicle 105. The throttle may beconfigured to control, for instance, the operating speed of the engineand, in turn, control the speed of the vehicle 105. The brake unit caninclude any combination of mechanisms configured to decelerate thevehicle 105. The brake unit can use friction to slow the wheels in astandard manner. In other embodiments, the brake unit may convert thekinetic energy of the wheels to electric current. The brake unit maytake other forms as well. The navigation unit may be any systemconfigured to determine a driving path or route for the vehicle 105. Thenavigation unit may additionally be configured to update the drivingpath dynamically while the vehicle 105 is in operation. In someembodiments, the navigation unit may be configured to incorporate datafrom the prediction-based trajectory planning module 200, the GPStransceiver, and one or more predetermined maps so as to determine thedriving path for the vehicle 105. The autonomous control unit mayrepresent a control system configured to identify, evaluate, and avoidor otherwise negotiate potential obstacles in the environment of thevehicle 105. In general, the autonomous control unit may be configuredto control the vehicle 105 for operation without a driver or to providedriver assistance in controlling the vehicle 105. In some embodiments,the autonomous control unit may be configured to incorporate data fromthe prediction-based trajectory planning module 200, the GPStransceiver, the RADAR, the LIDAR, the cameras, and other vehiclesubsystems to determine the driving path or trajectory for the vehicle105. The vehicle control system 146 may additionally or alternativelyinclude components other than those shown and described.

Occupant interface subsystems 148 may be configured to allow interactionbetween the vehicle 105 and external sensors, other vehicles, othercomputer systems, and/or an occupant or user of vehicle 105. Forexample, the occupant interface subsystems 148 may include standardvisual display devices (e.g., plasma displays, liquid crystal displays(LCDs), touchscreen displays, heads-up displays, or the like), speakersor other audio output devices, microphones or other audio input devices,navigation interfaces, and interfaces for controlling the internalenvironment (e.g., temperature, fan, etc.) of the vehicle 105.

In an example embodiment, the occupant interface subsystems 148 mayprovide, for instance, means for a user/occupant of the vehicle 105 tointeract with the other vehicle subsystems. The visual display devicesmay provide information to a user of the vehicle 105. The user interfacedevices can also be operable to accept input from the user via atouchscreen. The touchscreen may be configured to sense at least one ofa position and a movement of a user's finger via capacitive sensing,resistance sensing, or a surface acoustic wave process, among otherpossibilities. The touchscreen may be capable of sensing finger movementin a direction parallel or planar to the touchscreen surface, in adirection normal to the touchscreen surface, or both, and may also becapable of sensing a level of pressure applied to the touchscreensurface. The touchscreen may be formed of one or more translucent ortransparent insulating layers and one or more translucent or transparentconducting layers. The touchscreen may take other forms as well.

In other instances, the occupant interface subsystems 148 may providemeans for the vehicle 105 to communicate with devices within itsenvironment. The microphone may be configured to receive audio (e.g., avoice command or other audio input) from a user of the vehicle 105.Similarly, the speakers may be configured to output audio to a user ofthe vehicle 105. In one example embodiment, the occupant interfacesubsystems 148 may be configured to wirelessly communicate with one ormore devices directly or via a communication network. For example, awireless communication system could use 3G cellular communication, suchas CDMA, EVDO, GSM/GPRS, or 4G cellular communication, such as WiMAX orLTE. Alternatively, the wireless communication system may communicatewith a wireless local area network (WLAN), for example, using WIFI®. Insome embodiments, the wireless communication system 146 may communicatedirectly with a device, for example, using an infrared link, BLUETOOTH®,or ZIGBEE®. Other wireless protocols, such as various vehicularcommunication systems, are possible within the context of thedisclosure. For example, the wireless communication system may includeone or more dedicated short range communications (DSRC) devices that mayinclude public or private data communications between vehicles and/orroadside stations.

Many or all of the functions of the vehicle 105 can be controlled by thecomputing system 170. The computing system 170 may include at least onedata processor 171 (which can include at least one microprocessor) thatexecutes processing instructions stored in a non-transitory computerreadable medium, such as the data storage device 172. The computingsystem 170 may also represent a plurality of computing devices that mayserve to control individual components or subsystems of the vehicle 105in a distributed fashion. In some embodiments, the data storage device172 may contain processing instructions (e.g., program logic) executableby the data processor 171 to perform various functions of the vehicle105, including those described herein in connection with the drawings.The data storage device 172 may contain additional instructions as well,including instructions to transmit data to, receive data from, interactwith, or control one or more of the vehicle drive subsystem 140, thevehicle sensor subsystem 144, the vehicle control subsystem 146, and theoccupant interface subsystems 148.

In addition to the processing instructions, the data storage device 172may store data such as object processing parameters, training data,roadway maps, and path information, among other information. Suchinformation may be used by the vehicle 105 and the computing system 170during the operation of the vehicle 105 in the autonomous,semi-autonomous, and/or manual modes.

The vehicle 105 may include a user interface for providing informationto or receiving input from a user or occupant of the vehicle 105. Theuser interface may control or enable control of the content and thelayout of interactive images that may be displayed on a display device.Further, the user interface may include one or more input/output deviceswithin the set of occupant interface subsystems 148, such as the displaydevice, the speakers, the microphones, or a wireless communicationsystem.

The computing system 170 may control the function of the vehicle 105based on inputs received from various vehicle subsystems (e.g., thevehicle drive subsystem 140, the vehicle sensor subsystem 144, and thevehicle control subsystem 146), as well as from the occupant interfacesubsystem 148. For example, the computing system 170 may use input fromthe vehicle control system 146 in order to control the steering unit toavoid an obstacle detected by the vehicle sensor subsystem 144 andfollow a path or trajectory generated by the prediction-based trajectoryplanning module 200. In an example embodiment, the computing system 170can be operable to provide control over many aspects of the vehicle 105and its subsystems.

Although FIG. 1 shows various components of vehicle 105, e.g., vehiclesubsystems 140, computing system 170, data storage device 172, andprediction-based trajectory planning module 200, as being integratedinto the vehicle 105, one or more of these components could be mountedor associated separately from the vehicle 105. For example, data storagedevice 172 could, in part or in full, exist separate from the vehicle105. Thus, the vehicle 105 could be provided in the form of deviceelements that may be located separately or together. The device elementsthat make up vehicle 105 could be communicatively coupled together in awired or wireless fashion.

Additionally, other data and/or content (denoted herein as ancillarydata) can be obtained from local and/or remote sources by the in-vehiclecontrol system 150 as described above. The ancillary data can be used toaugment, modify, or train the operation of the prediction-basedtrajectory planning module 200 based on a variety of factors including,the context in which the user is operating the vehicle (e.g., thelocation of the vehicle, the specified destination, direction of travel,speed, the time of day, the status of the vehicle, etc.), and a varietyof other data obtainable from the variety of sources, local and remote,as described herein.

In a particular embodiment, the in-vehicle control system 150 and theprediction-based trajectory planning module 200 can be implemented asin-vehicle components of vehicle 105. In various example embodiments,the in-vehicle control system 150 and the prediction-based trajectoryplanning module 200 in data communication therewith can be implementedas integrated components or as separate components. In an exampleembodiment, the software components of the in-vehicle control system 150and/or the prediction-based trajectory planning module 200 can bedynamically upgraded, modified, and/or augmented by use of the dataconnection with the mobile devices 132 and/or the network resources 122via network 120. The in-vehicle control system 150 can periodicallyquery a mobile device 132 or a network resource 122 for updates orupdates can be pushed to the in-vehicle control system 150.

Prediction-Based System and Method for Trajectory Planning of AutonomousVehicles

As described in various example embodiments, a prediction-based systemand method for trajectory planning of autonomous vehicles are describedherein. Specifically, the present disclosure relates to trajectoryplanning for autonomous vehicles using a prediction-based method. In oneaspect, the system herein may include various sensors configured tocollect perception data, a computing device, and a trajectory predictionmodule for predicting a trajectory of other vehicles and/or dynamicobjects in the vicinity of (proximate to) the host autonomous vehicle.Initially, the computing device generates a trajectory option, while thetrajectory prediction module predicts reactions of vehicles and/ordynamic objects examined using data concerning likely trajectories ofeach vehicle and/or dynamic objects respectively. Data corresponding topredicted reactions can be sent to the computing device to perfect thetrajectory option suggested initially. The computing device maysubsequently instruct the trajectory prediction module to furthercollect data and conduct predictions to complete the trajectory planningprocess.

The disclosed embodiments take advantage of the perception information,which includes status and context information from a host autonomousvehicle to predict the behavior of the proximate vehicles that mighthave an influence on the host vehicle. An example embodiment usesmachine learning techniques to analyze the massive perception andcontext data recorded from the behavior of vehicles and drivers in realworld traffic environments. This analysis of the perception and contextdata enables the embodiments to accurately predict the behavior ofproximate vehicles and objects for a context in which the host vehicleis operating. Once the predicted behavior of proximate vehicles andobjects is determined, the example embodiments can use a motion planningprocess to generate the predicted trajectory for each of the proximatevehicles. The predicted trajectory for each of the proximate vehiclescan be compared with the desired or proposed trajectory for the hostvehicle and potential conflicts can be determined. The trajectory forthe host vehicle can be modified to avoid the potential conflicts withthe proximate vehicles. One purpose of the prediction-based system andmethod for trajectory planning is to avoid collision of the host vehiclewith other proximate vehicles and objects on the road. Other traditionalmethods for collision avoidance use only the historical information fromthe host vehicle itself. As described in detail herein, the variousembodiments use context information of the host vehicle and proximatevehicles to predict the behavior and trajectories of the vehicles basedon training data. As a result, the prediction-based trajectory planningsystem of the example embodiments can effectively control an autonomousvehicle in traffic.

Referring now to FIG. 2, an example embodiment disclosed herein can beused in the context of a prediction-based trajectory planning system 202for autonomous vehicles. In an example embodiment, the prediction-basedtrajectory planning system 202 can include a trajectory predictionmodule 175 (described in more detail below), which can be implemented asa machine learning system, neural network, or the like. As such, theexample embodiment can be implemented in two phases: an offline trainingphase and an operational phase. The training phase is used to train andconfigure the parameters of the machine learning system or neuralnetwork of the trajectory prediction module 175 or any other componentof the prediction-based trajectory planning system 202 implemented witha machine learning system or neural network. The operational phase isused after the machine learning components have been trained and areready to support the generation of predicted vehicle or objecttrajectories as described in more detail below.

Referring again to FIG. 2, the offline training phase for training andbuilding a prediction-based trajectory planning system in an exampleembodiment is illustrated. In the training phase, the training datacollection system 201 can be used to generate, train, and/or configurethe trajectory prediction module 175 or any other machine learningcomponents of the prediction-based trajectory planning system 202. Asdescribed in more detail below for an example embodiment, theprediction-based trajectory planning system 202 can use the trained andconfigured trajectory prediction module 175 during the operational phaseto generate predicted vehicle or object trajectories based on perceptiondata provided to the prediction-based trajectory planning system 202 andbased on the training the trajectory prediction module 175 receives fromthe training data collection system 201 during the training phase.

The training data collection system 201 can include a plurality oftraining data gathering mechanisms including obtaining training data ortraining images from a library or human driving database, and obtainingtraining data or training images from an array of perception informationgathering devices or sensors that may include image generating devices(e.g., cameras), light amplification by stimulated emission of radiation(laser) devices, light detection and ranging (LIDAR) devices, globalpositioning system (GPS) devices, sound navigation and ranging (sonar)devices, radio detection and ranging (radar) devices, and the like. Theperception information gathered by the information gathering devices atvarious traffic locations can include traffic or vehicle image data,roadway data, environmental data, distance data from LIDAR or radardevices, and other sensor information received from the informationgathering devices of the training data collection system 201 positionedadjacent to particular roadways (e.g., monitored locations).Additionally, the training data collection system 201 can includeinformation gathering devices installed in moving test vehicles beingnavigated through pre-defined routings in an environment or location ofinterest. The perception information can include data from which aposition and velocity of neighboring vehicles in the vicinity of orproximate to the autonomous vehicle or host vehicle can be obtained orcalculated. Corresponding ground truth data can also be gathered by thetraining data collection system 201. As a result, the perceptioninformation, ground truth data, and other available information can beobtained, processed, and used to build a training dataset for trainingand configuring the machine learning components of the prediction-basedtrajectory planning system 202.

The training data collection system 201 can thereby collect actualtrajectories of vehicles and corresponding ground truth data underdifferent scenarios and different driver actions and intentions in acontext. The different scenarios can correspond to different locations,different traffic patterns, different environmental conditions, and thelike. The scenarios can be represented, for example, by an occupancygrid, a collection of vehicle states on a map, or a graphicalrepresentation, such as a top-down image of one or more areas ofinterest. The driver actions, behaviors, and intentions can correspondto a driver's short term driving goals, such as turning left or right,accelerating or decelerating, merging, making right turn at anintersection, making a U-turn, and the like. The driver actions,behaviors, and intentions can also correspond to a set of driver orvehicle control actions to accomplish a particular short term drivinggoal.

The image data and other perception data, ground truth data, contextdata, and other training data collected by the training data collectionsystem 201 reflects truly realistic, real-world traffic informationrelated to the locations or routings, the scenarios, and the driveractions, behaviors, and intentions being monitored. Using the standardcapabilities of well-known data collection devices, the gathered trafficand vehicle image data and other perception or sensor data can bewirelessly transferred (or otherwise transferred) to a data processor ofa standard computing system, upon which the training data collectionsystem 201 can be executed. Alternatively, the gathered traffic andvehicle image data and other perception or sensor data can be stored ina memory device at the monitored location or in the test vehicle andtransferred later to the data processor of the standard computingsystem. The traffic and vehicle image data and other perception orsensor data, the ground truth data, the driver action and intentiondata, and other related data gathered or calculated by the training datacollection system 201 can be used to generate the training data, whichcan be used to build, train, and/or configure the trajectory predictionmodule 175 in the training phase. For example, as well-known, neuralnetworks or other machine learning systems can be trained to produceconfigured output based on training data provided to the neural networkor other machine learning system in a training phase. The training dataprovided by the training data collection system 201 can be used tobuild, train, and/or configure the trajectory prediction module 175 orany other machine learning components of the prediction-based trajectoryplanning system 202 to generate predicted vehicle or object trajectoriesgiven a current context and the training received during the trainingphase. As a result, the prediction-based trajectory planning system 202can use the trained trajectory prediction module 175 and the real-worldperception data 210 (shown in FIG. 6) in the operational phase togenerate proximate vehicle or object trajectories. Thus, the exampleembodiments use the training data collection system 201 to collectcontext data corresponding to human driving behaviors and then use theprediction-based trajectory planning system 202 and the trainedtrajectory prediction module 175 therein to generate predicted vehicletrajectories based on the human driving behaviors. Additionally duringthe training phase, the example embodiments can use a loss function toexamine and correct the results of the training provided to thetrajectory prediction module 175 by the training data collection system201. Because the trajectory prediction module 175 is trained in thetraining phase using real world, human behavior data, the predictedbehavior and trajectories of vehicles or objects produced by thetrajectory prediction module 175 are closely correlated to the actualbehavior and trajectories of vehicles in real world environments withhuman drivers and based on a human driver behavior model implemented bythe training data collection system 201.

FIG. 3 illustrates an example 401 of the context data used to train thetrajectory prediction module 175 in the offline training phase. In theexample 401, a host vehicle marked A is shown in a center lane of athree-lane roadway. As shown in the example 401, the host vehicle A canbe operating in a context in which vehicles or objects in any of sixproximate vehicles locations (P1, P2, . . . P6) can be operating nearbyor proximate to the location of the host vehicle A. In the example 401,a leading proximate vehicle P2 is driving in the same lane as the hostvehicle A and ahead of the host vehicle A. The following vehicle P5 isdriving in the same lane as the host vehicle A and behind the hostvehicle A. The proximate vehicle P1 is driving in the lane to the leftof the lane occupied by the host vehicle A and ahead of the host vehicleA. The proximate vehicle P3 is driving in the lane to the right of thelane occupied by the host vehicle A and ahead of the host vehicle A. Theproximate vehicle P4 is driving in the lane to the left of the laneoccupied by the host vehicle A and behind the host vehicle A. Theproximate vehicle P6 is driving in the lane to the right of the laneoccupied by the host vehicle A and behind the host vehicle A. As such,host vehicle A can be positioned in a context relative to the sixproximate vehicle locations (P1, P2, . . . P6) illustrated in FIG. 3. Itwill be apparent to those of ordinary skill in the art in view of thedisclosure herein that a different number of proximate vehicle positionscan be equivalently used to define a context for the host vehicle A.Moreover, it will be understood by those of ordinary skill in the art inview of the disclosure herein that not all of the proximate vehiclepositions may be occupied by an actual vehicle or object in a real worldscenario. In an example embodiment, a coordinate system (l, d) can beused to define the location of the host vehicle A relative to the sixproximate vehicle locations (P1, P2, . . . P6). In one embodiment, the 1axis can be aligned parallel with the lane markers of the roadway. The daxis can be oriented perpendicularly to the 1 axis and the lane markersof the roadway. As a result, the position of the host vehicle A relativeto the six proximate vehicle locations (P1, P2, . . . P6) can be readilydetermined. In an alternative embodiment, the position of the hostvehicle A and the six proximate vehicle locations (P1, P2, . . . P6) canbe represented with world coordinates, GPS coordinates, or the like. Itwill be apparent to those of ordinary skill in the art in view of thedisclosure herein that a different coordinate system can be equivalentlyused to define a position of the host vehicle A relative to the sixproximate vehicle locations (P1, P2, . . . P6). The coordinate systemprovides a convenient and accurate way to generate coordinatetransformations to virtually shift the position of the host vehicle A toor from any of the six proximate vehicle locations (P1, P2, . . . P6).As described in more detail below, this coordinate transformation isuseful for shifting perception data captured by the host vehicle A to acontext corresponding to any of the six proximate vehicle locations (P1,P2, . . . P6).

Referring now to FIG. 4, the context data used to train the trajectoryprediction module 175 in the offline training phase is furtherillustrated in an example embodiment. In the example 402, the proximatevehicle P5 is shown in a center lane of a three-lane roadway. A similarexample can apply to each of the six proximate vehicle locations (P1,P2, . . . P6). Vehicles or objects operating in each of the proximatevehicles locations (P1, P2, . . . P6) typically behave in three basicways with respect to heading or direction of travel and one basic waywith respect to rate or acceleration. As shown in FIG. 4, the threebasic directionality behaviors of the proximate vehicles with respect toheading or direction of travel are: 1) left turn, 2) straight [no turn],and 3) right turn. The one basic rate behavior of the proximate vehiclesis acceleration or deceleration. The directionality and rate behaviorsof the proximate vehicles can be used when the training data isgenerated to enable the trajectory prediction module 175 to learn andthus, predict the likely behavior of proximate vehicles based on thehuman driving data embodied in the training data. For example, imagesincluded in the training data can be labeled using human labelers orautomated processes to associate a label having behavior and directioninformation with each instance of a vehicle in the training data. In theexample embodiment, regression techniques can be used for accelerationprediction using the human driving data to produce a regressing modelfor acceleration prediction. As shown in FIG. 4, the labels or labelingdata can include context information that defines the directionality andrate behaviors of the vehicles represented in the training data. Whenthis training data is used to train the trajectory prediction module 175in the training phase, the trajectory prediction module 175 will retaininformation related to the context and behaviors of vehicles in realworld environments. This vehicle context and behavior information can beused by the trajectory prediction module 175 to infer the likelybehaviors of the proximate vehicles given a similar context. As aresult, the trajectory prediction module 175 can be trained andconfigured to perform intention, behavior, and trajectory predictionrelative to proximate vehicles.

FIG. 5 illustrates an operational or processing workflow 500 for theoffline training of the trajectory prediction module 175 in an exampleembodiment. In operation block 501, the prediction-based trajectoryplanning system 202 can receive training data including human drivingdata from the training data collection system 201 as described above.The prediction-based trajectory planning system 202 can then performfiltering and smoothing of the training data (operation block 503). Thesmoothing can include removing spurious or outlier data. Then inoperation block 505, context extraction is performed from the trainingdata including extraction of vehicle or object statistics and labeling(e.g., vehicle or object behavior with direction). An example embodimentcan use regression to predict acceleration (operation block 505).Finally, the training data collection system 201 can use the trainingdata and context data to train the trajectory prediction module 175 toperform intention, behavior, and trajectory prediction relative toproximate vehicles (operation block 507).

Referring now to FIG. 6, after the trajectory prediction module 175 ofthe prediction-based trajectory planning system 202 is trained in theoffline training phase as described above, the trajectory predictionmodule 175 can be deployed in an operational phase of theprediction-based trajectory planning system 202. In the operationalphase, the prediction-based trajectory planning system 202 can use thetrained trajectory prediction module 175 to generate predicted vehicleor object trajectories based on a human driver behavior model asdescribed above. The operational phase of the prediction-basedtrajectory planning system 202 is described in more detail below.

Referring again to FIG. 6, a diagram illustrates an example embodimentof the components of the prediction-based trajectory planning system 202and the prediction-based trajectory planning module 200 therein. In theexample embodiment, the prediction-based trajectory planning module 200can be configured to include a trajectory processing module 173 and thetrained trajectory prediction module 175. As described in more detailbelow, the trajectory processing module 173 serves to enable generationof a trajectory for the host vehicle (e.g., the autonomous vehicle). Thetrained trajectory prediction module 175 serves to enable generation ofpredicted trajectories of proximate vehicles near the host vehicle. Thevehicle trajectories can be generated based on input perception data 210received from one or more of the vehicle sensor subsystems 144,including one or more cameras, and processed by an image processingmodule to identify proximate agents (e.g., moving vehicles, dynamicobjects, or other objects in the proximate vicinity of the hostvehicle). The generated proximate vehicle trajectories are also based onthe training of the trajectory prediction module 175 by the trainingdata collection system 201 as described above. The trajectory processingmodule 173 and the trajectory prediction module 175 can be configured assoftware modules executed by the data processor 171 of the in-vehiclecontrol system 150. The modules 173 and 175 of the prediction-basedtrajectory planning module 200 can receive the input perception data 210and produce a trajectory 220, which can be used by the autonomouscontrol subsystem of the vehicle control subsystem 146 to moreefficiently and safely control the host vehicle 105. As part of theirtrajectory planning processing, the trajectory processing module 173 andthe trajectory prediction module 175 can be configured to work withtrajectory planning and prediction configuration parameters 174, whichcan be used to customize and fine tune the operation of theprediction-based trajectory planning module 200. The trajectory planningand prediction configuration parameters 174 can be stored in a memory172 of the in-vehicle control system 150.

In the example embodiment, the prediction-based trajectory planningmodule 200 can be configured to include an interface with the in-vehiclecontrol system 150, as shown in FIG. 1, through which theprediction-based trajectory planning module 200 can send and receivedata as described herein. Additionally, the prediction-based trajectoryplanning module 200 can be configured to include an interface with thein-vehicle control system 150 and/or other ecosystem 101 subsystemsthrough which the prediction-based trajectory planning module 200 canreceive ancillary data from the various data sources described above. Asdescribed above, the prediction-based trajectory planning module 200 canalso be implemented in systems and platforms that are not deployed in avehicle and not necessarily used in or with a vehicle.

In an example embodiment as shown in FIG. 6, the prediction-basedtrajectory planning module 200 can be configured to include thetrajectory processing module 173 and the trained trajectory predictionmodule 175, as well as other processing modules not shown for clarity.Each of these modules can be implemented as software, firmware, or otherlogic components executing or activated within an executable environmentof the prediction-based trajectory planning module 200 operating withinor in data communication with the in-vehicle control system 150. Each ofthese modules of an example embodiment is described in more detail belowin connection with the figures provided herein.

As a result of the processing performed by the prediction-basedtrajectory planning system 202, data corresponding to predicted orsimulated vehicle behaviors and predicted or simulated vehicle or objecttrajectories can be produced and fed back into the prediction-basedtrajectory planning system 202 to improve the accuracy of the predictedtrajectories. Ultimately, the improved prediction-based trajectoryplanning system 202 can be used to provide highly accurate predictedtraffic trajectory information to a user or for configuration of acontrol system of an autonomous vehicle. In a particular example, thepredicted or simulated traffic trajectory information can be provided toa system component used to create a virtual world where a control systemfor an autonomous vehicle can be trained and improved. The virtual worldis configured to be identical (as possible) to the real world wherevehicles are operated by human drivers. In other words, the predicted orsimulated traffic trajectory information generated by theprediction-based trajectory planning system 202 is directly andindirectly useful for configuring the control systems for an autonomousvehicle. It will be apparent to those of ordinary skill in the art thatthe prediction-based trajectory planning system 202 and the predicted orsimulated traffic trajectory information described and claimed hereincan be implemented, configured, processed, and used in a variety ofother applications and systems as well.

Referring again to FIG. 6, the prediction-based trajectory planningmodule 200, and the trajectory processing module 173 therein, canreceive input perception data 210 from one or more of the vehicle sensorsubsystems 144, including one or more cameras. The image data from thevehicle sensor subsystems 144 can be processed by an image processingmodule to identify proximate agents or other objects (e.g., movingvehicles, dynamic objects, or other objects in the proximate vicinity ofthe vehicle 105). The process of semantic segmentation can be used forthis purpose. The information related to the identified proximate agentsor other objects can be received by the prediction-based trajectoryplanning module 200, and the trajectory processing module 173 therein,as input perception data 210. The trajectory processing module 173 canuse the input perception data 210 as part of a trajectory planningprocess. In particular, the trajectory processing module 173 caninitially generate a first proposed trajectory for the host autonomousvehicle 105. The first proposed trajectory can correspond to aparticular path for navigating the vehicle 105 to a desired waypoint ordestination. The first proposed trajectory can also correspond to aparticular path for controlling the vehicle 105 to avoid obstaclesdetected in the proximity of the host vehicle 105. The first proposedtrajectory can also correspond to a particular path for directing thehost vehicle 105 to perform a particular action, such as passing anothervehicle, adjusting speed or heading to maintain separation from othervehicles, maneuvering the vehicle in turns, performing a controlledstop, or the like. In each of these cases, the first proposed trajectorymay cause the host vehicle 105 to make a sequential change to its speedand/or heading. As a result of changes in the host vehicle's 105 speedor heading, other agents or vehicles on the roadway proximate to thehost vehicle 105 may react to the host vehicle's 105 change in speed,heading, other action, and/or context. The trained trajectory predictionmodule 175 is provided in an example embodiment to anticipate or predictthe likely actions or reactions of the proximate agents to the hostvehicle's 105 change in context (e.g., speed, heading, or the like).Thus, the trajectory processing module 173 can provide the firstproposed trajectory of the host vehicle 105 in combination with thepredicted trajectories of proximate agents produced by the trajectoryprediction module 175. The trajectory prediction module 175 can generatethe likely trajectories, or a distribution of likely trajectories ofproximate agents, which are predicted to result from the context of thehost vehicle 105 (e.g., following the first proposed trajectory). Theselikely or predicted trajectories of proximate agents can be determinedbased on the machine learning techniques configured from the trainingscenarios produced from prior real-world human driver behavior modeldata collections gathered and assimilated into training data using thetraining data collection system 201 as described above. These likely orpredicted trajectories can also be configured or tuned using theconfiguration data 174. Over the course of collecting data from manyhuman driver behavior model driving scenarios and training machinedatasets and rule sets (or neural nets or the like), the likely orpredicted trajectories of proximate agents can be determined with avariable level of confidence or probability. The confidence level orprobability value related to a particular predicted trajectory can beretained or associated with the predicted trajectory of each proximateagent detected to be near the host vehicle 105 at a point in timecorresponding to the desired execution of the first proposed trajectory.The trajectory prediction module 175 can generate these predictedtrajectories and confidence levels for each proximate agent relative tothe context of the host vehicle 105. The trajectory prediction module175 can generate the predicted trajectories and corresponding confidencelevels for each proximate agent as an output relative to the context ofthe host vehicle 105. The trajectory processing module 173 can use thepredicted trajectories and corresponding confidence levels for eachproximate agent as generated by the trajectory prediction module 175 todetermine if any of the predicted trajectories for the proximate agentsmay cause the host vehicle 105 to violate a pre-defined goal based on arelated score being below a minimum acceptable threshold. The trajectoryprocessing module 173 can score the first proposed trajectory as relatedto the predicted trajectories for any of the proximate agents. The scorefor the first proposed trajectory relates to the level to which thefirst proposed trajectory complies with pre-defined goals for the hostvehicle 105, including safety, efficiency, legality, passenger comfort,and the like. Minimum score thresholds for each goal can be pre-defined.For example, score thresholds related to turning rates, acceleration orstopping rates, speed, spacing, etc. can be pre-defined and used todetermine if a proposed trajectory for host vehicle 105 may violate apre-defined goal. If the score for the first proposed trajectory, asgenerated by the trajectory processing module 173 based on the predictedtrajectories for any of the proximate agents, may violate a pre-definedgoal, the trajectory processing module 173 can reject the first proposedtrajectory and the trajectory processing module 173 can generate asecond proposed trajectory. The second proposed trajectory and thecurrent context of the host vehicle 105 can be provided to thetrajectory prediction module 175 for the generation of a new set ofpredicted trajectories and confidence levels for each proximate agent asrelated to the second proposed trajectory and the context of the hostvehicle 105. The new set of predicted trajectories and confidence levelsfor each proximate agent as generated by the trajectory predictionmodule 175 can be output from the trajectory prediction module 175 andprovided to the trajectory processing module 173. Again, the trajectoryprocessing module 173 can use the predicted trajectories and confidencelevels for each proximate agent corresponding to the second proposedtrajectory to determine if any of the predicted trajectories for theproximate agents may cause the vehicle 105 to violate a pre-defined goalbased on a related score being below a minimum acceptable threshold. Ifthe score for the second proposed trajectory, as generated by thetrajectory processing module 173 based on the new set of predictedtrajectories for any of the proximate agents, may violate a pre-definedgoal, the trajectory processing module 173 can reject the secondproposed trajectory and the trajectory processing module 173 cangenerate a third proposed trajectory. This process can be repeated untila proposed trajectory generated by the trajectory processing module 173and processed by the trajectory prediction module 175 results inpredicted trajectories and confidence levels for each proximate agentthat cause the proposed trajectory for the host vehicle 105 to satisfythe pre-defined goals based on the a related score being at or above aminimum acceptable threshold. Alternatively, the process can be repeateduntil a time period or iteration count is exceeded. If the process of anexample embodiment as described above results in predicted trajectories,confidence levels, and related scores that satisfy the pre-definedgoals, the corresponding proposed trajectory 220 is provided as anoutput from the prediction-based trajectory planning module 200 as shownin FIG. 6.

FIGS. 7 and 8 illustrate an operational or processing workflow 600 forthe operational phase use of the prediction-based trajectory planningsystem 202 in an example embodiment. In operation block 601 shown inFIG. 7, the prediction-based trajectory planning system 202 receivesperception data 210 from a host vehicle. The perception data can includesensor data from sensors (e.g., cameras, LIDAR, radar, etc.) mounted onor used in connection with the host vehicle 105. In operation block 603,the prediction-based trajectory planning system 202 can use the receivedperception data to generate coordinate transformations of the perceptiondata relative to vehicles proximate to the host vehicle. In a particularembodiment, the position of each proximate vehicle relative to the hostvehicle can be determined. The coordinates can be transformed to worldcoordinates or the (l,d) coordinate system as described above. As aresult, the perception data from the host vehicle can be used todetermine a context of each of the proximate vehicles near the hostvehicle. The transformed perception data for the proximate vehicles canbe filtered and smoothed in operation block 605. The data smoothing caninclude removing noise, spurious data, and outlier data. In a particularembodiment, a Gaussian filter can be used. Once the transformedperception data is filtered and smoothed, feature and context extractionfrom the data can be performed in operation block 607. The feature andcontext extraction is performed from the transformed and filteredperception data over a pre-determined time period to obtain host vehiclefeature data (e.g., position and velocity, etc.) and proximate vehiclecontext data (e.g., position and velocity of proximate vehicles, etc.).Having obtained the host vehicle feature data and the proximate vehiclecontext data, the prediction-based trajectory planning system 202 canemploy the trained trajectory prediction module 175 or any other machinelearning components to generate intention or behavior and trajectoryprediction relative to the proximate vehicles as described above(operation block 609). Based on the intention or behavior and trajectorypredictions relative to the proximate vehicles, the trajectoryprediction module 175 can generate predicted trajectories for each ofthe proximate vehicles using a specific motion planning process based onthe predicted intention or behavior of each proximate vehicle (operationblock 611). For example, the trained trajectory prediction module 175may determine that a particular proximate vehicle is likely to execute aleft accelerating turn based on the training data and the vehiclecontext. In this particular example, the trained trajectory predictionmodule 175 may correspondingly generate a trajectory for the particularproximate vehicle including a path and speed/acceleration for the leftaccelerating turn. Processing for the operational or processing workflow600 shown in FIG. 7 then continues at the connector labeled “A” shown inFIG. 8.

Referring now to FIG. 8, the operational or processing workflow 600continues at the connector labeled “A.” In operation block 611 shown inFIG. 7, the trajectory prediction module 175 generated predictedtrajectories for each of the proximate vehicles based on the predictedintention or behavior of each proximate vehicle. At this point, anexample embodiment can generate a proposed trajectory for the hostvehicle in operation block 613. If the proposed trajectory for the hostvehicle generated in operation block 613 will conflict with any of thepredicted trajectories of the proximate vehicles generated in operationblock 611 (i.e., the “Yes” branch of decision block 615), processingcontrol returns to operation block 613 where the proposed trajectory forthe host vehicle is revised, modified, and/or re-generated. A conflictoccurs when any of the predicted trajectories of the proximate vehicleswould intersect with or approach too closely to the proposed trajectoryof the host vehicle. The prediction-based trajectory planning system 202will enforce safety zones and thresholds to make sure that safety isobserved, conflicts are eliminated, and collisions are avoided. The loopbetween operation block 613 and decision block 615 will continue untilthe proposed trajectory for the host vehicle generated in operationblock 613 does not conflict with any of the predicted trajectories ofthe proximate vehicles generated in operation block 611 (i.e., the “No”branch of decision block 615). In this case, processing control passesto operation block 617 where the proposed trajectory for the hostvehicle is executed and control of host vehicle is directed along theproposed trajectory, which will not be in conflict with any of theproximate vehicles.

Referring now to FIG. 9, a flow diagram illustrates an exampleembodiment of a system and method 1000 for providing prediction-basedtrajectory planning of autonomous vehicles. The example embodiment canbe configured to: receive training data and ground truth data from atraining data collection system, the training data including perceptiondata and context data corresponding to human driving behaviors(processing block 1010); perform a training phase for training atrajectory prediction module using the training data (processing block1020); receive perception data associated with a host vehicle(processing block 1030); and perform an operational phase for extractinghost vehicle feature data and proximate vehicle context data from theperception data, using the trained trajectory prediction module togenerate predicted trajectories for each of one or more proximatevehicles near the host vehicle, generating a proposed trajectory for thehost vehicle, determining if the proposed trajectory for the hostvehicle will conflict with any of the predicted trajectories of theproximate vehicles, and modifying the proposed trajectory for the hostvehicle until conflicts are eliminated (processing block 1040).

Alternative Embodiment of the Prediction-Based System and Method forTrajectory Planning of Autonomous Vehicles

As described in various example embodiments, a prediction-based systemand method for trajectory planning of autonomous vehicles are describedherein. In particular example embodiments, the system herein may includevarious sensors configured to collect perception data, a computingdevice, and a trained trajectory prediction module for predicting atrajectory of other vehicles and/or dynamic objects in the vicinity of(proximate to) the host autonomous vehicle. In example embodimentsdescribed above, the computing device uses the trained trajectoryprediction module to predict trajectories of vehicles and/or dynamicobjects proximate to the host autonomous vehicle based on training dataincluding human driver behavior data. Then, the example embodimentsdescribed above generate a proposed trajectory for the host vehicle andcheck for conflicts between the proposed host vehicle trajectory and anyof the predicted trajectories of the proximate vehicles. If conflictsare detected, the proposed host vehicle trajectory is modifiedaccordingly.

In an alternative embodiment of the system and methods described herein,the alternative embodiments can first generate a proposed trajectory forthe host vehicle and then use the trained trajectory prediction moduleto predict trajectories of vehicles and/or dynamic objects proximate tothe host autonomous vehicle, if the host vehicle were to traverse theproposed trajectory. In this manner, the trained trajectory predictionmodule can generate the predicted trajectories of the proximate vehiclesas the proximate vehicles would likely react to the proposed hostvehicle trajectory. As in the example embodiments described above, thealternative embodiments can also check for conflicts between theproposed host vehicle trajectory and any of the predicted trajectoriesof the proximate vehicles. If conflicts are detected, the proposed hostvehicle trajectory is modified accordingly.

In support of the alternative example embodiments, the training of thetrajectory prediction module 175 during the offline training phase canbe slightly modified. In particular, the training data collection system201 can collect human driving data that classifies human drivingbehavior data into at least one of a plurality of different scenariosbased on particular actions performed by the vehicle. For example, thetraining data can include data representing vehicle actions such as,turning left or right, maintaining a straight direction, accelerating ordecelerating, performing an accelerating or decelerating turn, passinganother vehicle, or the like. The labeling data described above can beused to represent these different driving scenarios and actions.Additionally, the training data can include data representing actionstaken by proximate vehicles in reaction to the different drivingscenarios and actions performed by a test vehicle. As a result, thetraining data can include human driver behavior data related to howdrivers typically react to actions performed by proximate vehicles. Thisdata can be used in the alternative embodiments to determine or predictthe likely actions or reactions by proximate vehicles when the hostvehicle performs a particular action or follows a particular trajectory.This training data can be used by the trained trajectory predictionmodule 175 during the operational phase in the alternative embodimentsto anticipate the likely actions of proximate vehicles if the hostvehicle executes a particular proposed trajectory. By anticipating orpredicting the likely actions or reactions of proximate vehicles, thealternative embodiments can determine if the proposed host vehicletrajectory would be safe and not conflict with any of the predictedtrajectories of the proximate vehicles. Based on this determination, theproposed host vehicle trajectory can be modified, if necessary, toeliminate any conflicts with proximate vehicles.

Referring now to FIG. 10, a flow diagram illustrates the alternativeexample embodiment of a system and method 1001 for providingprediction-based trajectory planning of autonomous vehicles. The exampleembodiment can be configured to: receive training data and ground truthdata from a training data collection system, the training data includingperception data and context data corresponding to human drivingbehaviors (processing block 1011); perform a training phase for traininga trajectory prediction module using the training data (processing block1021); receive perception data associated with a host vehicle(processing block 1031); and perform an operational phase for extractinghost vehicle feature data and proximate vehicle context data from theperception data, generating a proposed trajectory for the host vehicle,using the trained trajectory prediction module to generate predictedtrajectories for each of one or more proximate vehicles near the hostvehicle based on the proposed host vehicle trajectory, determining ifthe proposed trajectory for the host vehicle will conflict with any ofthe predicted trajectories of the proximate vehicles, and modifying theproposed trajectory for the host vehicle until conflicts are eliminated(processing block 1041).

As used herein and unless specified otherwise, the term “mobile device”includes any computing or communications device that can communicatewith the in-vehicle control system 150 and/or the prediction-basedtrajectory planning module 200 described herein to obtain read or writeaccess to data signals, messages, or content communicated via any modeof data communications. In many cases, the mobile device 130 is ahandheld, portable device, such as a smart phone, mobile phone, cellulartelephone, tablet computer, laptop computer, display pager, radiofrequency (RF) device, infrared (IR) device, global positioning device(GPS), Personal Digital Assistants (PDA), handheld computers, wearablecomputer, portable game console, other mobile communication and/orcomputing device, or an integrated device combining one or more of thepreceding devices, and the like. Additionally, the mobile device 130 canbe a computing device, personal computer (PC), multiprocessor system,microprocessor-based or programmable consumer electronic device, networkPC, diagnostics equipment, a system operated by a vehicle 119manufacturer or service technician, and the like, and is not limited toportable devices. The mobile device 130 can receive and process data inany of a variety of data formats. The data format may include or beconfigured to operate with any programming format, protocol, or languageincluding, but not limited to, JavaScript, C++, iOS, Android, etc.

As used herein and unless specified otherwise, the term “networkresource” includes any device, system, or service that can communicatewith the in-vehicle control system 150 and/or the prediction-basedtrajectory planning module 200 described herein to obtain read or writeaccess to data signals, messages, or content communicated via any modeof inter-process or networked data communications. In many cases, thenetwork resource 122 is a data network accessible computing platform,including client or server computers, websites, mobile devices,peer-to-peer (P2P) network nodes, and the like. Additionally, thenetwork resource 122 can be a web appliance, a network router, switch,bridge, gateway, diagnostics equipment, a system operated by a vehicle119 manufacturer or service technician, or any machine capable ofexecuting a set of instructions (sequential or otherwise) that specifyactions to be taken by that machine. Further, while only a singlemachine is illustrated, the term “machine” can also be taken to includeany collection of machines that individually or jointly execute a set(or multiple sets) of instructions to perform any one or more of themethodologies discussed herein. The network resources 122 may includeany of a variety of providers or processors of network transportabledigital content. Typically, the file format that is employed isExtensible Markup Language (XML), however, the various embodiments arenot so limited, and other file formats may be used. For example, dataformats other than Hypertext Markup Language (HTML)/XML or formats otherthan open/standard data formats can be supported by various embodiments.Any electronic file format, such as Portable Document Format (PDF),audio (e.g., Motion Picture Experts Group Audio Layer 3—MP3, and thelike), video (e.g., MP4, and the like), and any proprietary interchangeformat defined by specific content sites can be supported by the variousembodiments described herein.

The wide area data network 120 (also denoted the network cloud) usedwith the network resources 122 can be configured to couple one computingor communication device with another computing or communication device.The network may be enabled to employ any form of computer readable dataor media for communicating information from one electronic device toanother. The network 120 can include the Internet in addition to otherwide area networks (WANs), cellular telephone networks, metro-areanetworks, local area networks (LANs), other packet-switched networks,circuit-switched networks, direct data connections, such as through auniversal serial bus (USB) or Ethernet port, other forms ofcomputer-readable media, or any combination thereof. The network 120 caninclude the Internet in addition to other wide area networks (WANs),cellular telephone networks, satellite networks, over-the-air broadcastnetworks, AM/FM radio networks, pager networks, UHF networks, otherbroadcast networks, gaming networks, WiFi networks, peer-to-peernetworks, Voice Over IP (VoIP) networks, metro-area networks, local areanetworks (LANs), other packet-switched networks, circuit-switchednetworks, direct data connections, such as through a universal serialbus (USB) or Ethernet port, other forms of computer-readable media, orany combination thereof. On an interconnected set of networks, includingthose based on differing architectures and protocols, a router orgateway can act as a link between networks, enabling messages to be sentbetween computing devices on different networks. Also, communicationlinks within networks can typically include twisted wire pair cabling,USB, Firewire, Ethernet, or coaxial cable, while communication linksbetween networks may utilize analog or digital telephone lines, full orfractional dedicated digital lines including T1, T2, T3, and T4,Integrated Services Digital Networks (ISDNs), Digital User Lines (DSLs),wireless links including satellite links, cellular telephone links, orother communication links known to those of ordinary skill in the art.Furthermore, remote computers and other related electronic devices canbe remotely connected to the network via a modem and temporary telephonelink.

The network 120 may further include any of a variety of wirelesssub-networks that may further overlay stand-alone ad-hoc networks, andthe like, to provide an infrastructure-oriented connection. Suchsub-networks may include mesh networks, Wireless LAN (WLAN) networks,cellular networks, and the like. The network may also include anautonomous system of terminals, gateways, routers, and the likeconnected by wireless radio links or wireless transceivers. Theseconnectors may be configured to move freely and randomly and organizethemselves arbitrarily, such that the topology of the network may changerapidly. The network 120 may further employ one or more of a pluralityof standard wireless and/or cellular protocols or access technologiesincluding those set forth herein in connection with network interface712 and network 714 described in the figures herewith.

In a particular embodiment, a mobile device 132 and/or a networkresource 122 may act as a client device enabling a user to access anduse the in-vehicle control system 150 and/or the prediction-basedtrajectory planning module 200 to interact with one or more componentsof a vehicle subsystem. These client devices 132 or 122 may includevirtually any computing device that is configured to send and receiveinformation over a network, such as network 120 as described herein.Such client devices may include mobile devices, such as cellulartelephones, smart phones, tablet computers, display pagers, radiofrequency (RF) devices, infrared (IR) devices, global positioningdevices (GPS), Personal Digital Assistants (PDAs), handheld computers,wearable computers, game consoles, integrated devices combining one ormore of the preceding devices, and the like. The client devices may alsoinclude other computing devices, such as personal computers (PCs),multiprocessor systems, microprocessor-based or programmable consumerelectronics, network PC's, and the like. As such, client devices mayrange widely in terms of capabilities and features. For example, aclient device configured as a cell phone may have a numeric keypad and afew lines of monochrome LCD display on which only text may be displayed.In another example, a web-enabled client device may have a touchsensitive screen, a stylus, and a color LCD display screen in which bothtext and graphics may be displayed. Moreover, the web-enabled clientdevice may include a browser application enabled to receive and to sendwireless application protocol messages (WAP), and/or wired applicationmessages, and the like. In one embodiment, the browser application isenabled to employ HyperText Markup Language (HTML), Dynamic HTML,Handheld Device Markup Language (HDML), Wireless Markup Language (WML),WMLScript, JavaScript™, EXtensible HTML (xHTML), Compact HTML (CHTML),and the like, to display and send a message with relevant information.

The client devices may also include at least one client application thatis configured to receive content or messages from another computingdevice via a network transmission. The client application may include acapability to provide and receive textual content, graphical content,video content, audio content, alerts, messages, notifications, and thelike. Moreover, the client devices may be further configured tocommunicate and/or receive a message, such as through a Short MessageService (SMS), direct messaging (e.g., Twitter), email, MultimediaMessage Service (MMS), instant messaging (IM), internet relay chat(IRC), mIRC, Jabber, Enhanced Messaging Service (EMS), text messaging,Smart Messaging, Over the Air (OTA) messaging, or the like, betweenanother computing device, and the like. The client devices may alsoinclude a wireless application device on which a client application isconfigured to enable a user of the device to send and receiveinformation to/from network resources wirelessly via the network.

The in-vehicle control system 150 and/or the prediction-based trajectoryplanning module 200 can be implemented using systems that enhance thesecurity of the execution environment, thereby improving security andreducing the possibility that the in-vehicle control system 150 and/orthe prediction-based trajectory planning module 200 and the relatedservices could be compromised by viruses or malware. For example, thein-vehicle control system 150 and/or the prediction-based trajectoryplanning module 200 can be implemented using a Trusted ExecutionEnvironment, which can ensure that sensitive data is stored, processed,and communicated in a secure way.

FIG. 11 shows a diagrammatic representation of a machine in the exampleform of a computing system 700 within which a set of instructions whenexecuted and/or processing logic when activated may cause the machine toperform any one or more of the methodologies described and/or claimedherein. In alternative embodiments, the machine operates as a standalonedevice or may be connected (e.g., networked) to other machines. In anetworked deployment, the machine may operate in the capacity of aserver or a client machine in server-client network environment, or as apeer machine in a peer-to-peer (or distributed) network environment. Themachine may be a personal computer (PC), a laptop computer, a tabletcomputing system, a Personal Digital Assistant (PDA), a cellulartelephone, a smartphone, a web appliance, a set-top box (STB), a networkrouter, switch or bridge, or any machine capable of executing a set ofinstructions (sequential or otherwise) or activating processing logicthat specify actions to be taken by that machine. Further, while only asingle machine is illustrated, the term “machine” can also be taken toinclude any collection of machines that individually or jointly executea set (or multiple sets) of instructions or processing logic to performany one or more of the methodologies described and/or claimed herein.

The example computing system 700 can include a data processor 702 (e.g.,a System-on-a-Chip (SoC), general processing core, graphics core, andoptionally other processing logic) and a memory 704, which cancommunicate with each other via a bus or other data transfer system 706.The mobile computing and/or communication system 700 may further includevarious input/output (I/O) devices and/or interfaces 710, such as atouchscreen display, an audio jack, a voice interface, and optionally anetwork interface 712. In an example embodiment, the network interface712 can include one or more radio transceivers configured forcompatibility with any one or more standard wireless and/or cellularprotocols or access technologies (e.g., 2nd (2G), 2.5, 3rd (3G), 4th(4G) generation, and future generation radio access for cellularsystems, Global System for Mobile communication (GSM), General PacketRadio Services (GPRS), Enhanced Data GSM Environment (EDGE), WidebandCode Division Multiple Access (WCDMA), LTE, CDMA2000, WLAN, WirelessRouter (WR) mesh, and the like). Network interface 712 may also beconfigured for use with various other wired and/or wirelesscommunication protocols, including TCP/IP, UDP, SIP, SMS, RTP, WAP,CDMA, TDMA, UMTS, UWB, WiFi, WiMax, Bluetooth™, IEEE 802.11x, and thelike. In essence, network interface 712 may include or support virtuallyany wired and/or wireless communication and data processing mechanismsby which information/data may travel between a computing system 700 andanother computing or communication system via network 714.

The memory 704 can represent a machine-readable medium on which isstored one or more sets of instructions, software, firmware, or otherprocessing logic (e.g., logic 708) embodying any one or more of themethodologies or functions described and/or claimed herein. The logic708, or a portion thereof, may also reside, completely or at leastpartially within the processor 702 during execution thereof by themobile computing and/or communication system 700. As such, the memory704 and the processor 702 may also constitute machine-readable media.The logic 708, or a portion thereof, may also be configured asprocessing logic or logic, at least a portion of which is partiallyimplemented in hardware. The logic 708, or a portion thereof, mayfurther be transmitted or received over a network 714 via the networkinterface 712. While the machine-readable medium of an exampleembodiment can be a single medium, the term “machine-readable medium”should be taken to include a single non-transitory medium or multiplenon-transitory media (e.g., a centralized or distributed database,and/or associated caches and computing systems) that store the one ormore sets of instructions. The term “machine-readable medium” can alsobe taken to include any non-transitory medium that is capable ofstoring, encoding or carrying a set of instructions for execution by themachine and that cause the machine to perform any one or more of themethodologies of the various embodiments, or that is capable of storing,encoding or carrying data structures utilized by or associated with sucha set of instructions. The term “machine-readable medium” canaccordingly be taken to include, but not be limited to, solid-statememories, optical media, and magnetic media.

The Abstract of the Disclosure is provided to allow the reader toquickly ascertain the nature of the technical disclosure. It issubmitted with the understanding that it will not be used to interpretor limit the scope or meaning of the claims. In addition, in theforegoing Detailed Description, it can be seen that various features aregrouped together in a single embodiment for the purpose of streamliningthe disclosure. This method of disclosure is not to be interpreted asreflecting an intention that the claimed embodiments require morefeatures than are expressly recited in each claim. Rather, as thefollowing claims reflect, inventive subject matter lies in less than allfeatures of a single disclosed embodiment. Thus, the following claimsare hereby incorporated into the Detailed Description, with each claimstanding on its own as a separate embodiment.

1.-20. (canceled)
 21. A system comprising: a data processor; and aprediction-based trajectory planning module, executable by the dataprocessor, the prediction-based trajectory planning module beingconfigured to: receive perception data associated with a host vehicle;extract host vehicle feature data and proximate vehicle context datafrom the perception data; generate a proposed trajectory for the hostvehicle; use a trained trajectory prediction module to generatepredicted trajectories for each of one or more proximate vehicles, thetrained trajectory prediction module having been trained using trainingdata comprising perception data and context data corresponding to humandriving behaviors, the predicted trajectories for each of the one ormore proximate vehicles being in reaction to the proposed host vehicletrajectory; and modify the proposed trajectory for the host vehicle ifthe proposed trajectory for the host vehicle will conflict with any ofthe predicted trajectories of the one or more proximate vehicles. 22.The system of claim 21 being further configured to filter and smooth theperception data.
 23. The system of claim 21 being further configured togenerate coordinate transformations of the perception data relative tothe one or more proximate vehicles.
 24. The system of claim 21 whereinthe training data comprises labeling data that comprises contextinformation defining directionality and rate behaviors of vehiclesrepresented in the training data.
 25. The system of claim 21 wherein thetraining data comprises labeling data that comprises context informationdefining directionality and rate behaviors of vehicles represented inthe training data, the context data further defining a left turn, noturn, or a right turn.
 26. The system of claim 21 wherein modifying theproposed trajectory for the host vehicle comprises: determining if theproposed trajectory for the host vehicle will conflict with any of thepredicted trajectories of the one or more proximate vehicles; andmodifying the proposed trajectory for the host vehicle based on thedetermined conflicts until the conflicts are eliminated.
 27. The systemof claim 21 being further configured to use regression to predictacceleration of a vehicle.
 28. The system of claim 21 being furtherconfigured to determine if any of the predicted trajectories for the oneor more proximate vehicles may cause the host vehicle to violate apre-defined goal based on a related score being below a minimumacceptable threshold.
 29. The system of claim 21 wherein the proposedtrajectory for the host vehicle is output to a vehicle control subsystemcausing the host vehicle to follow the output proposed trajectory.
 30. Amethod comprising: receiving perception data associated with a hostvehicle; extracting host vehicle feature data and proximate vehiclecontext data from the perception data; generating a proposed trajectoryfor the host vehicle; using a trained trajectory prediction module togenerate predicted trajectories for each of one or more proximatevehicles, the trained trajectory prediction module having been trainedusing training data comprising perception data and context datacorresponding to human driving behaviors, the predicted trajectories foreach of the one or more proximate vehicles being in reaction to theproposed host vehicle trajectory; and modifying the proposed trajectoryfor the host vehicle if the proposed trajectory for the host vehiclewill conflict with any of the predicted trajectories of the one or moreproximate vehicles.
 31. The method of claim 30 wherein the proximatevehicle context data comprises proximate vehicle position and proximatevehicle velocity.
 32. The method of claim 30 comprising determining aposition of each proximate vehicle relative to the host vehicle.
 33. Themethod of claim 30 comprising obtaining perception data from an array ofperception information gathering devices or sensors.
 34. The method ofclaim 30 wherein the training data comprises labeling data obtained fromhuman labelers or automated labeling processes.
 35. The method of claim30 wherein the perception data comprising data received from a sensorfrom the group consisting of: a camera or image capture device, a GlobalPositioning System (GPS) transceiver, and a laser range finder/LIDARunit.
 36. The method of claim 30 comprising predicting acceleration of avehicle.
 37. The method of claim 30 comprising determining if any of thepredicted trajectories for the one or more proximate vehicles may causethe host vehicle to violate a pre-defined goal.
 38. The method of claim30 comprising causing the host vehicle to follow the proposedtrajectory.
 39. A non-transitory machine-useable storage mediumembodying instructions which, when executed by a machine, cause themachine to: receive perception data associated with a host vehicle;extract host vehicle feature data and proximate vehicle context datafrom the perception data; generate a proposed trajectory for the hostvehicle; use a trained trajectory prediction module to generatepredicted trajectories for each of one or more proximate vehicles, thetrained trajectory prediction module having been trained using trainingdata comprising perception data and context data corresponding to humandriving behaviors, the predicted trajectories for each of the one ormore proximate vehicles being in reaction to the proposed host vehicletrajectory; and modify the proposed trajectory for the host vehicle ifthe proposed trajectory for the host vehicle will conflict with any ofthe predicted trajectories of the one or more proximate vehicles. 40.The non-transitory machine-useable storage medium of claim 39 beingconfigured to generate predicted accelerations for each of the one ormore proximate vehicles near the host vehicle.